JP7559596B2 - Biological information processing device, biological information processing method, and program - Google Patents

Description

The present invention relates to a biometric information processing device, a biometric information processing method, and a program.

Conventionally, a bioinformation processing device is known that acquires a bioinformation signal using a sensor that detects respiration and heart rate to measure the respiration rate and heart rate of a living body, and detects periodicity from the bioinformation signal.

Radio wave sensors, pressure sensors, and sensors that use a combination of these are used as a means of acquiring bioinformation signals. In a normal living body state (unrestrained, etc.), it is difficult for the body to remain still, and some kind of movement is always occurring. When such a subject is used as a test subject, movements that are not being measured become noise, so a method is known in which body movements are removed using bandpass filters, etc.

In addition, a technology has been proposed that detects minute body surface movements based on the vibration of the measurement target, such as the subject's breathing or heart rate, by detecting the phase change (phase difference) of the reflected wave relative to the irradiated wave, and microwaves are used to efficiently detect the phase change (see Patent Documents 1 and 2).

In Patent Document 1, two bio-information signals, an I signal and a Q signal, are used separately to calculate the respiration rate, heart rate, etc., from the periodicity of the respective autocorrelation functions.

In Patent Document 2, periodicity is detected from the locus of points obtained by plotting the I and Q signal components on an IQ coordinate system, and respiratory rate, heart rate, etc. are calculated.

Figure 13 shows how points identified by the I and Q signals move over time on an I-Q plane. When the I and Q signals are signals representing breathing, in theory the locus of points identified by the I and Q signals rotates around the origin and moves back and forth on an arc in response to breathing. The movement of the points on this arc corresponds to exhalation and inhalation. The angle that the vector consisting of the components of the I and Q signals makes with the axis of the I signal is defined as θ, and the amount of displacement corresponding to breathing is calculated from the value of angle θ corresponding to each point on the locus, thereby detecting the periodicity of breathing.

Patent No. 6290501 JP 2018-064642 A

However, in Patent Document 1, because the two bio-information signals are processed separately, when calculating the respiration rate or heart rate from the periodic waveform, only one signal is used, and there is a risk that accurate values cannot be obtained. For example, if the period of the I signal or Q signal appears to be doubled, it is difficult to determine the exact period. In addition, because only a single signal is used in processing each of the two bio-information signals, the I signal and the Q signal, the signal itself is easily buried in noise. Furthermore, of the two bio-information signals, the I signal and the Q signal, the periodic characteristics of the bio-information signal may be clearly seen in only one of the I signal or the Q signal, and only noise may be detected in the other bio-information signal.

In addition, even with the technology described in Patent Document 2, when noise such as body movement is present, the point specified by the I and Q signals on the I-Q plane may not rotate around the origin in actual measurements, making it difficult to detect periodicity. For example, as shown in FIG. 14, the trajectories of the I and Q signals on the I-Q plane may not form a circular arc, but may form a loop. In particular, when using an electrically powered air mattress, the noise is large when detecting breathing with the air mattress sandwiched between the patient on the bed.

The present invention was made in consideration of the problems with the conventional technology described above, and aims to accurately calculate values related to the periodicity of biometric information.

In order to solve the above problem, the invention described in claim 1 is a bioinformation processing device that includes an acquisition means for acquiring N (N is an integer equal to or greater than 2) bioinformation signals measuring the same bioinformation, an analysis means for analyzing the autocorrelation of a locus of points specified by the N bioinformation signals in an N-dimensional space, and a calculation means for calculating a value relating to the periodicity of the bioinformation measured by determining the periodicity of the locus of points from the autocorrelation of the locus of points in the N-dimensional space.

The invention described in claim 2 is the biometric information processing device described in claim 1, in which the acquisition means acquires the biometric information signal from one or more radio wave sensors.

The invention described in claim 3 is the bioinformation processing device described in claim 2, in which the acquisition means acquires, as the bioinformation signal from the radio wave sensor, an I signal and a Q signal that is out of phase with the I signal by a predetermined amount.

The invention described in claim 4 is the bioinformation processing device described in claim 2 or 3, in which the acquisition means further acquires the bioinformation signal from one or more pressure-based sensors.

The invention described in claim 5 is the bioinformation processing device described in claim 1, in which the acquisition means acquires the bioinformation signal from two or more pressure sensors.

The invention described in claim 6 is a biometric information processing device described in any one of claims 1 to 5, in which the biometric information is respiration and/or heart rate.

The invention described in claim 7 is the bioinformation processing device described in any one of claims 1 to 6, wherein the analysis means analyzes the autocorrelation by adding values corresponding to the norm of a vector between two points on the locus of points in the N-dimensional space, a first point corresponding to a first time and a second point corresponding to a second time that is a predetermined time difference from the first time, while changing the first time, to obtain a sum, and by changing the predetermined time difference to obtain the sum for each of the predetermined time differences.

The invention described in claim 8 is the bio-information processing device described in claim 7, wherein the analysis means determines Z(τ) by equation (1) where t is the first time, I _j (t) (j=1 to N) is the N bio-information signals at t, τ is the predetermined time difference, k is an upper limit of the first time, and Z(τ) is the sum at τ.

The invention described in claim 9 is the biometric information processing device described in claim 7 or 8, in which the sum takes a minimum value when the predetermined time difference is an integer multiple of the period of the biometric information.

The invention described in claim 10 is a bioinformation processing method including an acquisition step of acquiring N (N is an integer equal to or greater than 2) bioinformation signals measuring the same bioinformation, an analysis step of analyzing the autocorrelation of a locus of points specified by the N bioinformation signals in an N-dimensional space, and a calculation step of calculating a value relating to the periodicity of the bioinformation measured by determining the periodicity of the locus of points from the autocorrelation of the locus of points in the N-dimensional space.

The invention described in claim 11 is a program for causing a computer to function as an acquisition means for acquiring N (N is an integer of 2 or more) bioinformation signals measuring the same bioinformation, an analysis means for analyzing the autocorrelation of a locus of points specified by the N bioinformation signals in an N-dimensional space, and a calculation means for calculating a value relating to the periodicity of the bioinformation measured by determining the periodicity of the locus of points from the autocorrelation of the locus of points in the N-dimensional space.

The present invention makes it possible to accurately calculate values related to the periodicity of biometric information.

FIG. 1 is a system configuration diagram of a biological information monitoring system. FIG. 2 is a block diagram showing a functional configuration of a station server. FIG. 2 is a block diagram showing a functional configuration of a bedside terminal. 13 is a flowchart showing a process executed by the bedside terminal. 1A is a diagram showing an example of a bioinformation signal, and FIG. 1B is a diagram showing an example of a PACF. FIG. 2 is a diagram showing an example of time changes of two bio-information signals (I signal/Q signal). FIG. 11 is a diagram for explaining recursive filter processing. 1 is a diagram showing the trajectories of I and Q signals on the IQ plane obtained by measuring respiration in Example 1. FIG. (a) is time series data of a respiratory waveform, (b) is time series data of an I signal, (c) is time series data of a Q signal, and (d) is a PACF created from the I and Q signals. 1A is a diagram showing the trajectories on the _I1 - _{Q1 plane of an I1 signal and a Q1 signal obtained by a first microwave sensor with breathing as a measurement target in Example 2. FIG. 1B is a diagram showing the trajectories on the I2-Q2 plane of an I2 signal} and a _Q2 signal obtained by a second microwave sensor with breathing as a measurement target in Example 2. (a) Time series data of respiratory waveform. (b) Time series data of _I1 signal. (c) Time series data of _Q1 signal. (d) Time series data of _I2 signal. (e) Time series data of _Q2 signal. (f) PACF created from _I1 signal, _Q1 signal, _I2 signal, and _Q2 signal. (a) is time series data of the respiratory waveform of Example 3. (b) is time series data of the _I1 signal. (c) is time series data of the _Q1 signal. (d) is time series data of the _I2 signal. (e) is time series data of the _Q2 signal. (f) is time series data of the signal output from the piezoelectric sensor. (g) is a PACF created from the _I1 signal, _Q1 signal, _I2 signal, _Q2 signal, and piezoelectric sensor signal. FIG. 1 is a diagram showing a theoretical locus of points specified by I and Q signals on an IQ plane. FIG. 13 is a diagram showing an example of an actual locus of points specified by I and Q signals on an IQ plane.

The following describes an embodiment of the present invention with reference to the drawings. However, the scope of the invention is not limited to the illustrated examples.

[Configuration of the biological information monitoring system]
FIG. 1 shows the system configuration of a biological information monitoring system 100 .
As shown in FIG. 1, the biological information monitoring system 100 includes a station server 10 provided in a nurse station, a bedside terminal 30 (biological information processing device) provided in each bed in a hospital room, and a mobile terminal 60 carried by a medical professional such as a nurse. The biological information monitoring system 100 is used in a medical facility such as a hospital. The station server 10 and the bedside terminal 30 can communicate data with each other by wireless communication. The station server 10 and the mobile terminal 60 can also communicate data with each other by wireless communication. It is preferable that the wireless communication in the medical facility uses a sub-gigahertz band that does not interfere with medical equipment. The number of bedside terminals 30 and mobile terminals 60 is not particularly limited.

The station server 10 centrally manages the measurement data (biological information) of each patient collected by the bedside terminal 30. The display unit 13 of the station server 10 displays a monitoring screen for monitoring the biological information of multiple patients. Examples of the biological information include respiratory rate, heart rate, body temperature, _SpO2 , etc. At the nurse station, the station server 10 can grasp changes in the condition of each patient. The station server 10 also transmits the measurement data of each patient to the mobile terminal 60. The station server 10 may also transmit the measurement data of each patient to a cloud server or the like connected via a communication network.

In addition, an external HDD (Hard Disk Drive) 20, a UPS (Uninterruptible Power Supply) 21, and a printer 22 are connected to the station server 10.
The external HDD 20 stores data such as biological information of each patient managed by the station server 10 .
The UPS 21 is a device that incorporates a device for storing power, such as a secondary battery, and is capable of supplying power at a set output for a certain period of time even if the power supply from the outside is cut off due to a power outage or the like.
The printer 22 prints the measurement data on paper, and prints various measurement data and the like displayed on the display unit 13 of the station server 10 .

The bedside terminal 30 is installed at each patient's bedside, acquires the patient's biometric information from sensor units 40A, 40B, such as a respiratory sensor and a pulse oximeter, and displays the measurement results. The bedside terminal 30 also reads an IC card 51 to acquire identification information of medical personnel, and acquires the patient's biometric information from a measuring device 52 compatible with the HR (Health Record) Joint (registered trademark), such as a thermometer. The bedside terminal 30 transmits the measurement data of the biometric information to the station server 10.

The mobile terminal 60 displays the measurement data (biometric information) of each patient sent from the station server 10. This allows medical staff to check changes in each patient's condition from outside the nurse's station. It is also possible to send the measurement data of each patient directly from the bedside terminal 30 to the mobile terminal 60.

[Station server configuration]
FIG. 2 shows the functional configuration of the station server 10.
As shown in FIG. 2, the station server 10 is configured with a control unit 11, an operation unit 12, a display unit 13, a wireless communication unit 14, a memory unit 15, a communication unit 16, I/F (interface) units 17 to 19, etc., and each unit is connected by a bus.

The control unit 11 is composed of a CPU (Central Processing Unit), RAM (Random Access Memory), etc., and controls the overall processing operations of each part of the station server 10. Specifically, the CPU reads out various processing programs stored in the storage unit 15, expands them into the RAM, and performs various processes in cooperation with the programs.

The operation unit 12 is configured with a keyboard equipped with cursor keys, letter and number input keys, various function keys, etc., and a pointing device such as a mouse, and outputs operation signals input by key operations on the keyboard or mouse operations to the control unit 11.

The display unit 13 is configured with a monitor such as an LCD (Liquid Crystal Display), and displays various screens according to the instructions of the display signal input from the control unit 11.

The wireless communication unit 14 is a wireless interface for transmitting and receiving data with the bedside terminal 30 and the mobile terminal 60 via wireless communication.

The storage unit 15 is composed of a HDD, a non-volatile semiconductor memory, etc., and stores various data. For example, the storage unit 15 stores measurement data of biological information received from the bedside terminal 30 in association with the patient. The storage unit 15 also stores patient information for each patient corresponding to each bedside terminal 30. The patient information includes patient identification information (patient name, patient ID), the patient's medical history, precautions, handover information, etc. The patient information is stored in the storage unit 15 in advance by inputting it in advance or by acquiring electronic medical record information from an external device. The information managed in the station server 10 may be stored in the storage unit 15 or in the external HDD 20.

The communication unit 16 is configured with a network interface, etc., and transmits and receives data to and from external devices connected via a communication network such as a LAN (Local Area Network), a WAN (Wide Area Network), or the Internet.

The I/F units 17 to 19 are interfaces for connecting to various devices. Here, the I/F units 17 to 19 are connected to an external HDD 20, a UPS 21, and a printer 22, respectively.

[Bedside terminal configuration]
FIG. 3 shows the functional configuration of the bedside terminal 30. As shown in FIG.
As shown in FIG. 3, the bedside terminal 30 is configured with a control unit 31, an operation unit 32, a display unit 33, a timing unit 34, a wireless communication unit 35, a memory unit 36, an I/F unit 37, a data input unit 38, etc., and each unit is connected by a bus.

The control unit 31 is composed of a CPU, RAM, etc., and controls the overall processing operations of each unit of the bedside terminal 30. Specifically, the CPU reads out various processing programs stored in the storage unit 36, expands them in the RAM, and performs various processes in cooperation with the programs.

The operation unit 32 is equipped with various switches, various function buttons, etc., and outputs operation signals from these to the control unit 31. The various function buttons include buttons for inputting the details of various treatments (sputum suction, temperature measurement, etc.), getting out of bed (toilet, bath, etc.), etc.

The display unit 33 is configured with an LCD or the like, and displays various screens according to the instructions of a display signal input from the control unit 31. For example, the display unit 33 displays a screen showing biological information (measurements, measurement waveforms, etc.) acquired from the sensor units 40A and 40B.

The clock unit 34 has a clock circuit (RTC: Real Time Clock) that counts the current date and time and outputs it to the control unit 31.

The wireless communication unit 35 is a wireless interface for transmitting and receiving data with the station server 10 via wireless communication.

The storage unit 36 is composed of a non-volatile semiconductor memory or the like, and stores various processing programs, parameters and files required for executing the programs, etc. The storage unit 36 stores biological information of the patient corresponding to the bedside terminal 30.

The memory unit 36 also stores patient identification information (patient name, patient ID) for the patient corresponding to the bedside terminal 30.

The I/F section 37 is an interface for connecting to the sensor units 40A and 40B, and receives biological information signals of the patient from the sensor units 40A and 40B.
3 illustrates a case where two sensor units 40A and 40B are connected to the bedside terminal 30, but the present invention is not limited to this example. For example, one or three or more sensor units may be connected to the bedside terminal 30, and one sensor unit connected to the bedside terminal 30 may include multiple sensors.

Each of the sensor units 40A and 40B is configured with a control unit 41, a sensor 42, a memory unit 43, an I/F unit 44, etc., and each unit is connected by a bus.

The control unit 41 is composed of a CPU, RAM, etc., and controls the overall processing operations of each part of the sensor units 40A and 40B. Specifically, the CPU reads out various processing programs stored in the memory unit 43, loads them into the RAM, and performs various processes in cooperation with the programs.

Sensor 42 is a sensor for measuring the patient's bioinformation and generates a bioinformation signal according to the type of sensor. As sensor 42, a sensor corresponding to the bioinformation to be measured is used, and examples of such sensors include a radio wave sensor and a pressure sensor.

A radio wave sensor is a non-contact sensor that transmits radio waves, receives the radio waves reflected by the object being measured, and detects the moving speed (motion) of the object being measured by the change in frequency that occurs between the transmitted wave and the reflected wave. Examples of radio wave sensors include microwave sensors and millimeter wave sensors. Other types of radio wave sensors include Doppler sensors and FMCW (Frequency Modulated Continuous Wave) sensors. Radio wave sensors output an I signal and a Q signal that differs in phase from the I signal by a predetermined amount. For example, the Q signal differs in phase from the I signal by 90°.

A pressure sensor is a contact sensor that detects the movement of an object by converting the pressure from the object into an electrical signal.

The storage unit 43 is composed of a non-volatile semiconductor memory or the like, and stores various processing programs, parameters and files required for executing the programs, and the like.
The I/F unit 44 performs data communication with the bedside terminal 30 .

The data input unit 38 of the bedside terminal 30 is composed of an NFC (Near Field Communication) reader. The data input unit 38 acquires identification information of the medical professional from an IC card 51 carried by the medical professional. The data input unit 38 also acquires biological information of the patient from an HR joint-compatible measuring device 52. For example, the information acquired by the data input unit 38 is associated with the current date and time acquired from the clock unit 34 and transmitted to the station server 10.

In this embodiment, the periodicity of the trajectory is calculated for the coordinate positions of measurement points in an N-dimensional space identified by N bioinformation signals acquired from a radio wave sensor (I signal, Q signal), a pressure sensor, etc.

As shown in Figure 14, it is known that the trajectory of the points on the I-Q plane identified by the I and Q signals representing breathing does not form an arc centered on the origin, but forms a loop, and yet moves back and forth on a relatively constant trajectory in sync with breathing. Also, it is characterized by the fact that the speed of movement of the points identified by the I and Q signals slows down near the point on the I-Q plane where inspiration and expiration switch.

The control unit 31 acquires N (N is an integer equal to or greater than 2) pieces of bioinformation signals (time-series data) that are the measurement target of the same bioinformation. That is, the control unit 31 functions as an acquisition unit.
The biological information may be, for example, respiration and/or heart rate. A case in which respiration is used as biological information will be described below. The control unit 31 acquires, via the I/F unit 37, N biological information signals output from the sensors 42 of the sensor units 40A and 40B with respiration as a measurement target.

The following types of sensors [1] to [7] can be used as respiration sensors.
[1] Radio wave sensor (see, for example, Japanese Patent No. 6290501)
Radio wave sensors include Doppler sensors (radio waves used: microwaves), FMCW sensors (radio waves used: microwaves, millimeter waves), pulse Doppler sensors (radio waves used: microwaves, millimeter waves), and UWB (pulse radar) (radio waves used: microwaves).

[2] Pressure-type sensor Examples of pressure-type sensors include piezoelectric sensors (see, for example, Japanese Patent No. 4,139,828), air pressure sensors (see, for example, Japanese Patent No. 2017-219341), and load sensors (see, for example, Japanese Patent No. 6,268,219).

[3] Thermistor (semiconductor temperature sensor)
The thermistor-based sensor is attached directly to the patient's nostrils and measures breathing by distinguishing between inhalation and exhalation from temperature changes caused by airflow.

[4] Thoracic impedance Sensors that use thoracic impedance pass a weak current between electrodes attached to the chest for electrocardiograms and detect changes in impedance as voltage changes.

[5] Acoustic: An adhesive sensor with a built-in acoustic transducer is attached to the patient's neck to measure the sound of air passing through the airway. This air sound is measured simultaneously with _SpO2 to identify breathing patterns (inhalation and exhalation).

[6] Capnography Capnography detects breathing by measuring the partial pressure of carbon dioxide in inhaled and exhaled air.

[7] Oximetry: Using data obtained by measuring _SpO2 and pulse rate using a conventional pulse oximeter, respiratory rate is calculated by taking advantage of the fact that baseline pulse wave changes due to changes in venous return caused by breathing.

When acquiring N bioinformation signals measuring the same bioinformation, the control unit 31 acquires, for example, bioinformation signals (I signal, Q signal) from one or more radio wave sensors. The control unit 31 further acquires bioinformation signals for the same bioinformation as the radio wave sensor from one or more pressure sensors.

Alternatively, when acquiring N bioinformation signals measuring the same bioinformation, the control unit 31 acquires the bioinformation signals from two or more pressure-based sensors.

The control unit 31 analyzes the autocorrelation of the locus of points identified by N bioinformation signals in an N-dimensional space. In other words, the control unit 31 is an analysis means. Autocorrelation is a measure of the degree to which the time series data to be processed matches data obtained by shifting the data by a predetermined time, and is a function of the shift time.

Specifically, the control unit 31 calculates a sum by adding values according to the norm of a vector between a first point corresponding to a first time and a second point corresponding to a second time that is a predetermined time difference from the first time on a locus of points specified by N bioinformation signals in an N-dimensional space, while changing the first time. The control unit 31 analyzes the autocorrelation by calculating the sum for each predetermined time difference while changing the predetermined time difference.

A norm is a value that has the concept of the length of a vector indicated by two points in N-dimensional data. The value according to the norm may be any value that corresponds to the magnitude relationship of the distance between two points in an N-dimensional space, and may not be a general norm. For example, the sum of the absolute values of the difference between each component between two points, the sum of the powers of the difference between each component between two points, etc., may be used as the value according to the norm, and it is not necessary to take the root of these values or multiply them by some value.
The predetermined time difference can also be zero.
That is, when the first time is t, the bioinformation signal at t is I(t), and the bioinformation signal at time (t+τ) shifted by a time difference τ from the first time t is I(t+τ), the norm may be |I(t)-I(t+τ)| or a higher order one such as (I(t)-I(t+τ)) ² .

In this embodiment, the control unit 31 calculates Z(τ) by equation (1), where t is a first time, I _j (t) (j=1 to N) are N bio-information signals at t, τ is a predetermined time difference, k is an upper limit of the first time, and Z(τ) is the sum at τ.

The value in the braces { } in equation (1) is the sum of the squares of the differences in signal values that differ by the time difference τ for each component of the N-dimensional data, so Z(τ) takes on values greater than or equal to 0, and is 0 when τ=0.

Z(τ) takes a minimum value (opposite to a general autocorrelation function) when the time difference τ coincides with an integer multiple of the period of the biometric information, so as shown in the following formula (2), Z(τ) is normalized to be in the range of 0 to 1, its sign is inverted, and 1 is added to it, which is defined as the PACF (Position AutoCorrelation Function). As a result, PACF(τ) takes a value between 0 and 1, and is 1 when τ = 0. PACF is a value that indicates the autocorrelation of the locus of points identified by N biometric information signals in an N-dimensional space.

The PACF value reflects the tendency of the entire measurement period, so the influence of both noise contained throughout the measurement and noise superimposed on a portion of the measurement (such as burst noise) is reduced. On the other hand, if the periodicity of the measurement object, such as breathing, is not uniform, inputting data from a measurement period that is too long compared to the period may result in inaccurate results because the value of the function PACF(τ) will not be large. Therefore, the data may be divided into periods equal to or several periods of the breathing period, and the measurement periods to be input may be determined separately. The measurement period may be variable according to the most recent measurement situation. The measurement period may also be slid while overlapping in parts. If the unit time for calculating the breathing rate is longer than the measurement period, the breathing rates in multiple measurement periods obtained within the most recent unit time may be added (if there is overlap, weighting may be applied according to the degree of overlap).

The control unit 31 calculates a value relating to the periodicity of the biological information being measured by determining the periodicity of the locus of points from the autocorrelation of the locus of points in N-dimensional space. In other words, the control unit 31 is a calculation means. The value relating to the periodicity is not particularly limited as long as it is information that represents the periodicity of the biological information. Examples of values relating to the periodicity include the period, frequency (vibration rate), respiratory rate (number of breaths per unit time), heart rate (number of heart beats per unit time), etc.

[Bedside terminal operation]
Next, the operation of the bedside terminal 30 will be described.
4 is a flowchart showing the processing executed by the bedside terminal 30. This processing is realized by software processing in cooperation with the CPU of the control unit 31 and a program stored in the storage unit 36.

The control unit 31 acquires N bio-information signals (I signal, Q signal of the radio wave sensor, etc.) measuring respiration from the sensor units 40A, 40B via the I/F unit 37 (step S1). Note that the N bio-information signals are continuously output from the sensor units 40A, 40B, and the control unit 31 sets the range of the time series data to be used for processing to a predetermined time going back from the present.

Next, the control unit 31 performs pre-processing on each of the acquired N bio-information signals (step S2).
The pre-processing includes clutter calibration correction, body movement detection, bed exit detection, band-pass filter processing, and the like.

Clutter calibration correction is a process that removes non-measurement movement (noise) from the signal. For example, if the signal value is outside a predetermined range, it is corrected to fall within that range.

Body movement detection and bed leaving detection are processes that detect body movement and getting out of bed based on the speed and amplitude of the vital signs signals (I signal, Q signal, etc.). Signals that are different from those at rest are not used to calculate the respiratory rate.

Bandpass filtering is a process that extracts only a specific frequency band from a signal (e.g., passband: 4 to 150 bpm).

Next, the control unit 31 performs a periodic signal calculation process on the N bioinformation signals after the preprocessing (step S3).
The periodic signal calculation process includes trend removal process (step S31), PACF calculation process (step S32), reliability index calculation process (step S33), and recursive filter process (step S34).

The trend removal process (step S31) is a process for removing trend components (fluctuation components with relatively low frequency) contained in the bioinformation signal.

(PACF calculation process)
The PACF calculation process (step S32) is a process of calculating the PACF from the N number of biological information signals by the above-mentioned formulas (1) and (2). Here, a specific method of calculating the PACF will be described.

<One-dimensional case>
N is an integer of 2 or more, but to facilitate understanding of Z(τ) and PACF(τ), the case of N=1 will be described first. In the one-dimensional case, Z(τ) defined in the above formula (1) is rewritten as the following formula (3), where I(t) is the bioinformation signal at time t.

FIG. 5A shows an example of a signal I(t) acquired as a biological information signal.
(A) A signal I(t+τ ₁ ), which is delayed by a time difference τ ₁ with respect to the waveform of a signal I(t), has a waveform shifted to the left by the time difference τ ₁ from I(t), as shown in FIG. 5( a ).
(B) The sum Z(τ ₁ ) of the squares of the differences between I(t) and I(t+τ ₁ ) at time t is calculated along the time t.
(C) While changing the time difference _τ1 , the processes (A) and (B) are repeated in order for a certain interval (one period or more), and the sum Z(τ) is calculated for each time difference τ.

(D) PACF(τ) is determined by inverting the sign of normalized Z(τ) according to the above formula (2) and adding 1 to the result. An example of PACF(τ) is shown in FIG.
The waveform of the frequencies contained in the signal I(t) is obtained as PACF(τ). The noise in the PACF(τ) is reduced compared to the signal I(t).

<In the case of two dimensions>
In the two-dimensional case, Z(τ) defined in the above formula (1) can be rewritten as the following formula (4), where I(t) and Q(t) are the I signal and Q signal obtained from the radio wave sensor at time t.

An example of the time change of two bio-information signals (I signal/Q signal) is shown in Fig. 6. In Fig. 6, time t is taken in the direction perpendicular to the IQ plane.
Consider signals I(t+τ) and Q(t+τ) which are delayed by a time difference τ from the waveform depicted by signals I(t) and Q(t).
While changing the time t, add up (I(t) - I(t + τ)) ² + (Q(t) - Q(t + τ)) ² to find the sum Z(τ) at the time difference τ.
(I(t) - I(t + τ)) ^² + (Q(t) - Q(t + τ)) ^² is a value that corresponds to the square of the distance between the point identified by (I(t), Q(t)) and the point identified by (I(t + τ), Q(t + τ)) on the IQ plane.

After calculating Z(τ) according to the above formula (4), calculate PACF(τ) according to the above formula (2).

<In the case of four dimensions>
In the case of four dimensions, Z(τ) defined in the above formula (1) can be rewritten as the following formula (5), where I ₁ (t), Q ₁ (t), I ₂ (t), and Q ₂ (t) are the I and Q signals obtained from two radio wave sensors, respectively.

As shown in equation (5), the sum Z(τ) at the time difference τ is calculated using the difference between a point identified by four bio-information signals in a four-dimensional space and a point at a time that differs by a predetermined time difference τ.
After Z(τ) is calculated according to the above formula (5), PACF(τ) is calculated according to the above formula (2).

(Reliability index calculation process)
The reliability index calculation process (step S33) is a process of calculating a reliability index for the PACF. The reliability index is a value indicating the reliability of data.

When calculating the reliability index, for example, the following method is used.
(A) In the case of PACF, if the total number of peaks within a certain period or the interval between individual peaks falls outside the respiration measurement specification range (e.g., 4-50 bpm), the reliability index is set to 0.
(B) If the variance of the peak intervals is greater than a predetermined value, the reliability index is decreased.
(C) In PACF, the maximum value at τ=0 is defined as the first peak, and the next maximum value is defined as the second peak. If the height of the second peak is lower than that of the first peak, it is determined that the autocorrelation is small, and the reliability index is reduced.

(Recursive filter processing)
The recursive filter process (step S34) is a process of taking an arithmetic average using data acquired at multiple times for time series data, and is used for noise reduction in videos, etc. Specifically, the latest data and past data are added together at a predetermined ratio and treated as the processed data.

The recursive filter process for the PACF will be described with reference to Fig. 7. The latest PACF shown in Fig. 7 is a PACF calculated from the latest measurement data (time series data). The PACF for calculating the respiration rate is a PACF reflecting data up to the previous PACF in terms of time. For example, the value of the latest PACF x 0.1 + the PACF for calculating the respiration rate x 0.9 is updated as the next PACF for calculating the respiration rate.
The initial value of the PACF for calculating the respiratory rate is set to a constant value of 0.

If the reliability index obtained from the latest PACF is low, noise is removed by reducing the ratio of the latest PACF used in the recursive filter processing.

When body movement or getting out of bed is detected consecutively, the latest PACF is determined to have a low reliability index, and the respiration rate, etc. calculated based on the old PACF will continue to be displayed on the display unit 33. To avoid this, it is desirable to reduce the ratio of PACF for calculating the respiration rate used in the recursive filter processing when body movement or getting out of bed is detected.

After step S3, the control unit 31 performs a periodicity determination process on the recursively filtered PACF (step S4).
The periodicity determination process includes a peak detection process (step S41), an individual respiratory cycle calculation process (step S42), a noise removal process for the respiratory cycle (step S43), and a respiratory rate calculation process (step S44).

The peak detection process (step S41) is a process for detecting peaks (maximum and minimum values) from the PACF waveform after recursive filter processing.

The individual respiratory cycle calculation process (step S42) is a process for finding the time between adjacent peaks (between maximum values and between minimum values) as individual respiratory cycles (peak intervals). Specifically, the control unit 31 calculates the time from the first peak (maximum value at τ=0) to the second peak (second maximum value), the time from the second peak to the third peak (third maximum value), the time from the third peak to the fourth peak (fourth maximum value), etc. as periods.

The noise removal process for the respiratory cycle (step S43) is a process for removing noise from each calculated respiratory cycle. For example, the largest two and smallest two pieces of data from each respiratory cycle (peak interval) are removed.

The respiratory rate calculation process (step S44) calculates the average respiratory cycle after noise has been removed, and calculates the respiratory rate from this average. If the average respiratory cycle is T (seconds), then the respiratory rate is 60/T (bpm).

The control unit 31 causes the respiration rate calculated in this manner to be displayed on the display unit 33. The respiration rate value displayed on the display unit 33 is updated at the sampling interval at which the bioinformation signals are acquired from the sensor units 40A and 40B.

The timing (phase) at which the value of a signal measured from a living body increases or decreases varies depending on the time point at which the data is acquired.
In contrast, since the PACF is defined to have a maximum value at τ=0 as shown in formulas (1) and (2), when the PACF is graphed, the maximum peak position is always at the left end (τ=0). Therefore, no matter when the data (biological information signal) is acquired, the phase of the PACF (timing of increase and decrease in value) is aligned at τ=0. Because the PACF has such a characteristic, a recursive filter can be applied to PACFs that start acquiring data at different times.

Example 1
In the first embodiment, a case will be described in which two bio-information signals output from one microwave sensor (I signal, Q signal) are used.
Fig. 8 shows the trajectories of I and Q signals on the IQ plane obtained by measuring breathing. In Fig. 8, the direction from state A to state B corresponds to exhalation, and the direction from state B to state A corresponds to inhalation.

Fig. 9(a) shows time series data of the respiratory waveform, Fig. 9(b) and (c) show time series data of the I signal and the Q signal, respectively, and Fig. 9(d) shows the PACF created from the I signal and the Q signal. The horizontal axis of Fig. 9(a) to (c) is time t, and the horizontal axis of Fig. 9(d) is time difference τ. Fig. 9(a) to (c) show positions corresponding to state A and state B in the graph.
9A is not actually measured data, but is intended to indicate where in the respiratory waveform each bioinformation signal corresponds. The same applies to the second and third embodiments.

In actuality, signals obtained from sensors are often buried in noise, making it difficult to detect the frequency (vibration rate). Even when such signals are included, by using PACF, which reflects the overall movement of multiple signals, it is possible to accurately detect the frequency of the biological information of the measurement target (equivalent to the number of breaths per unit time in respiratory data).

As shown in Figures 8 and 9(c), the Q signal used in Example 1 also increases and then decreases in value when moving from state A to state B, and also increases and then decreases when moving from state B to state A. In other words, the value of the Q signal actually increases and decreases twice during one round trip along the trajectory on the I-Q plane. In this way, even when the Q signal or I signal moves at twice the frequency of the respiration being measured, the PACF is not affected.

In addition, since the PACF always starts from its maximum value at τ = 0 (= the phase is aligned), a recursive filter can be applied to PACFs obtained at different times, which has the advantage of making it easier to reduce noise.

Example 2
In the second embodiment, a case will be described in which four bio-information signals output from two microwave sensors (I signal/Q signal×2) are used.
Fig. 10(a) shows the trajectories of the _I1 signal and the Q1 signal on the _I1 - _{Q1 plane} obtained by the first microwave sensor with breathing as the measurement target. Fig. 10(b) shows the trajectories of the _I2 signal and the Q2 signal on the _I2 - _{Q2 plane} obtained by the second microwave sensor with breathing as the measurement target. In Figs. 10(a) and (b), the direction from state A to state B corresponds to exhalation, and the direction from state B to state A corresponds to inhalation.

Fig. 11(a) shows time series data of the respiratory waveform, Fig. 11(b) and (c) show time series data of the _I1 signal and the _Q1 signal, Fig. 11(d) and (e) show time series data of the _I2 signal and the _Q2 signal, Fig. 11(f) shows the PACF created from the _I1 signal, the _Q1 signal, the _I2 signal, and the _Q2 signal. The horizontal axis of Fig. 11(a) to (e) is time t, and the horizontal axis of Fig. 11(f) is time difference τ. Fig. 11(a) to (e) show positions corresponding to state A and state B in the graph.

Even when the number of sensors that measure certain biological information and output biological information signals is increased, the method of analyzing autocorrelation of biological information using PACF can be easily applied.
Generally, the signal-to-noise ratio of the output signal changes depending on the relative positions of the subject and the sensor, so it is desirable to place sensors in multiple positions so that even if the patient moves, one of the sensors will correctly capture the patient.
For example, as shown in FIG. 11( d ), even if the amplitude of the _I2 signal output from the second microwave sensor is small and buried in noise, resulting in a poor S/N ratio, by using PACF, it is possible to comprehensively detect the periodic breathing movement of the subject from the four signals output from the first microwave sensor and the second microwave sensor.

Example 3
In the third embodiment, a case will be described in which five bio-information signals output from two microwave sensors (I signal/Q signal×2) and one piezoelectric sensor are used.
The trajectories of the _I1 signal and Q1 signal on the _I1 - _Q1 plane obtained by the first microwave sensor with breathing as the measurement object, and the trajectories of the _I2 signal and _Q2 signal on the _I2 - _Q2 plane obtained by the second _microwave sensor with breathing as the measurement object are similar to those shown in Figures 10(a) and (b).

Fig. 12(a) shows time series data of the respiratory waveform, Fig. 12(b) and (c) show time series data of the _I1 signal and the _Q1 signal, Fig. 12(d) and (e) show time series data of the _I2 signal and the _Q2 signal, Fig. 12(f) shows time series data of the signal output from the piezoelectric sensor with breathing as the measurement target, Fig. 12(g) shows a PACF created from the _I1 signal, the _Q1 signal, the _I2 signal, the _Q2 signal, and the piezoelectric sensor signal. The horizontal axis of Fig. 12(a) to (f) is time t, and the horizontal axis of Fig. 12(g) is time difference τ. Fig. 12(a) to (f) show positions corresponding to state A and state B in the graph.

Even when combining sensors with different phases (the timing of increase and decrease in value), such as a microwave sensor and a piezoelectric sensor, it is possible to detect the frequency of the biological information of the subject from the overall movement of all the signals output from each sensor.

As described above, according to this embodiment, the autocorrelation of the locus of points specified by N bioinformation signals in an N-dimensional space can be used to accurately calculate a value related to the periodicity of bioinformation. With a single bioinformation signal, if there is a lot of noise or if the signal shows an increase or decrease that appears to be double the period, it may not be possible to obtain the correct period or frequency. However, with this embodiment, it is possible to comprehensively detect periodicity from multiple bioinformation signals.

Furthermore, on a locus of points identified by N bio-information signals in an N-dimensional space, the autocorrelation of the locus of points can be found by using the sum of values corresponding to the norm of vectors between two points that differ by a predetermined time difference.
Since the sum of the values according to the norm is a value equal to or greater than 0, the "time difference" at which this sum is at a minimum value can be determined to be an integer multiple of the period of the biological signal.

Specifically, by using Z(τ) defined in the above formula (1), it is possible to calculate the value related to the periodicity of the biological information with high accuracy.

Furthermore, since PACF(τ) defined in the above formula (2) includes a term with the sign of Z(τ) normalized to be in the range of 0 to 1 inclusive, PACF(τ) is a function of τ that takes values equal to or less than a predetermined value, and reaches a maximum value when τ = 0. Furthermore, by setting the predetermined value to 1 so that the value of PACF(τ) is in the range of 0 to 1 inclusive, PACF(τ) = 1 when τ = 0. In this way, by using a new periodic signal that reflects the periodicity of the biometric information, it is possible to detect the periodicity of the biometric information with high accuracy.

The above description of the embodiment is an example of the biometric information processing device according to the present invention, and is not limited to this. The detailed configuration and detailed operation of each part constituting the device can be modified as appropriate without departing from the spirit of the present invention.

For example, in the above embodiment, the bedside terminal 30 calculates a value related to the periodicity of breathing (respiratory rate). However, if multiple bio-information signals measuring the same bio-information can be acquired in the sensor units 40A, 40B connected to the bedside terminal 30, the sensor units 40A, 40B may calculate a value related to the periodicity of the bio-information (period, frequency, respiratory rate, heart rate, etc.).
Furthermore, the station server 10 may use data collected from the bedside terminal 30 to calculate a value relating to the periodicity of the biological information.
Furthermore, a signal processing device that has a PACF calculation unit and calculates a periodic value may be provided independently at a location capable of communicating with the sensor units 40A and 40B.

In addition, in the above formula (2), the range of the normalized value for Z(τ) is set to 0 to 1, and 1 is added to the normalized Z(τ), but in calculating PACF(τ), the range of the normalized value for Z(τ) and the value added to the normalized Z(τ) are not limited to the above example.

In addition, in order to equalize the influence of the bioinformation signals acquired from each sensor on the calculation of the PACF, the signal waveforms of each sensor may be normalized and used to calculate the PACF. For normalization, for example, the average value of the signal values is first subtracted from the signal waveform to make the amplitude centered on 0, and then the signal waveform amplitude is made uniform by dividing by the standard deviation of the signal values. Alternatively, the amplitude of the signal waveform may be set to 1. In particular, when using different types of sensors (such as a microwave sensor and a piezoelectric sensor) as in Example 3, it is desirable to normalize the individual signal values.

Furthermore, a bio-information signal that correctly captures the patient's movements, i.e., a highly reliable bio-information signal, can have a greater influence on the calculation of PACF than other bio-information signals, thereby improving measurement accuracy.
For example, a different weight is set for each bioinformation signal, and a normalized signal is multiplied by the weight to generate a signal, which is then used to calculate the PACF. In this case, a highly reliable bioinformation signal is identified from the signals output from each sensor, and the weight of the highly reliable bioinformation signal is increased.
Furthermore, it is desirable to determine that a biological information signal having a small variance (variance) in the peak intervals of the signal waveform is highly reliable and to increase the influence it has on the calculation of the PACF.

The programs for executing each process in each device may be stored on a portable recording medium. A carrier wave may also be used as a medium for providing program data via a communication line.

REFERENCE SIGNS LIST 10 Station server 30 Bedside terminal 31 Control unit 32 Operation unit 33 Display unit 35 Wireless communication unit 36 Storage unit 37 I/F unit 40A, 40B Sensor unit 42 Sensor 100 Biological information monitoring system

Claims (11)

an acquisition means for acquiring N (N is an integer of 2 or more) biological information signals having the same biological information as a measurement target;
an analysis means for analyzing an autocorrelation of a locus of points specified by the N bioinformation signals in an N-dimensional space;
a calculation means for calculating a value relating to the periodicity of the biological information to be measured by determining the periodicity of the locus of points from the autocorrelation of the locus of points in the N-dimensional space;
A biometric information processing device comprising: The biometric information processing device according to claim 1, wherein the acquisition means acquires the biometric information signal from one or more radio wave sensors. The bioinformation processing device according to claim 2, wherein the acquisition means acquires, from the radio wave sensor, an I signal and a Q signal that is out of phase with the I signal by a predetermined amount as the bioinformation signal. The bioinformation processing device according to claim 2 or 3, wherein the acquisition means further acquires the bioinformation signal from one or more pressure-based sensors. The bioinformation processing device according to claim 1, wherein the acquisition means acquires the bioinformation signal from two or more pressure sensors. The biometric information processing device according to any one of claims 1 to 5, wherein the biometric information is respiration and/or heart rate. The bioinformation processing device according to any one of claims 1 to 6, wherein the analysis means analyzes the autocorrelation by adding up values corresponding to the norm of a vector between two points on the locus of points in the N-dimensional space, the two points being a first point corresponding to a first time and a second point corresponding to a second time that is a predetermined time difference from the first time, while changing the first time, to obtain a sum, and by changing the predetermined time difference to obtain the sum for each of the predetermined time differences. The bioinformation processing device according to claim 7, wherein the analysis means determines Z(τ) by equation (1) using t as the first time, I _j (t) (j=1 to N) as the N bioinformation signals at t, τ as the predetermined time difference, k as an upper limit of the first time, and Z(τ) as the sum at τ.

The biometric information processing device according to claim 7 or 8, wherein the sum takes a minimum value when the predetermined time difference is an integer multiple of the period of the biometric information. an acquisition step of acquiring N (N is an integer of 2 or more) biological information signals of the same biological information to be measured;
an analysis step of analyzing an autocorrelation of a locus of points specified by the N bioinformation signals in an N-dimensional space;
a calculation step of calculating a value relating to the periodicity of the biological information to be measured by determining the periodicity of the locus of points from the autocorrelation of the locus of points in the N-dimensional space;
A biological information processing method comprising: Computer,
An acquisition means for acquiring N (N is an integer of 2 or more) biological information signals having the same biological information as a measurement target;
an analysis means for analyzing an autocorrelation of a locus of points specified by the N number of biological information signals in an N-dimensional space;
a calculation means for calculating a value relating to the periodicity of the biological information to be measured by determining the periodicity of the locus of points from the autocorrelation of the locus of points in the N-dimensional space;
A program to function as a

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